In recent years, advancements have been made towards the goal of using
chaotic coverage path planners for autonomous search and traversal of spaces
with limited environmental cues. However, the state of this field is still in
its infancy as there has been little experimental work done. Current
experimental work has not developed robust methods to satisfactorily address
the immediate set of problems a chaotic coverage path planner needs to overcome
in order to scan realistic environments within reasonable coverage times. These
immediate problems are as follows: (1) an obstacle avoidance technique which
generally maintains the kinematic efficiency of the robot's motion, (2) a means
to spread chaotic trajectories across the environment (especially crucial for
large and/or complex-shaped environments) that need to be covered, and (3) a
real-time coverage calculation technique that is accurate and independent of
cell size. This paper aims to progress the field by proposing algorithms that
address all of these problems by providing techniques for obstacle avoidance,
chaotic trajectory dispersal, and accurate coverage calculation. The algorithms
produce generally smooth chaotic trajectories and provide high scanning
coverage of environments. These algorithms were created within the ROS
framework and make up a newly developed chaotic path planning application. The
performance of this application was comparable to that of a conventional
optimal path planner. The performance tests were carried out in environments of
various sizes, shapes, and obstacle densities, both in real-life and Gazebo
simulations.

本文章提出了一种基于 ROS 框架的混沌路径规划应用程序，该程序通过提供避障技术、混沌轨迹分散和准确覆盖计算等技术，解决了对于限制目标的自主搜索与遍历任务的三大关键问题，其性能可与传统最优路径规划器相媲美，并在多种大小、形状和障碍物密度的真实环境与 Gazebo 模拟中进行了测试。